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Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F11%3A00073202%21RIV15-MV0-14330___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
The recent techniques for approximate similarity search focus on optimizing answer precision/recall and they typically improve the average of these measures over a set of sample queries. However, according to our observation, the recall for particular indexes and queries can fluctuate considerably. In order to stabilize the recall, we propose a query-evaluation model that exploits several variants of the search index. This approach is applicable to a signicant subset of current approximate methods with a focus on techniques based purely on metric postulates. Applying this approach to the M-Index structure, we perform extensive measurements on large datasets and we show that this approach has a positive impact on the recall stability and it suppresses the most unsatisfactory cases. Further, the results indicate that the proposed approach can also increase the general average recall for given overall search costs. The recent techniques for approximate similarity search focus on optimizing answer precision/recall and they typically improve the average of these measures over a set of sample queries. However, according to our observation, the recall for particular indexes and queries can fluctuate considerably. In order to stabilize the recall, we propose a query-evaluation model that exploits several variants of the search index. This approach is applicable to a signicant subset of current approximate methods with a focus on techniques based purely on metric postulates. Applying this approach to the M-Index structure, we perform extensive measurements on large datasets and we show that this approach has a positive impact on the recall stability and it suppresses the most unsatisfactory cases. Further, the results indicate that the proposed approach can also increase the general average recall for given overall search costs.
dcterms:title
Stabilizing the Recall in Similarity Search Stabilizing the Recall in Similarity Search
skos:prefLabel
Stabilizing the Recall in Similarity Search Stabilizing the Recall in Similarity Search
skos:notation
RIV/00216224:14330/11:00073202!RIV15-MV0-14330___
n4:aktivita
n9:S n9:P
n4:aktivity
P(GA201/09/0683), P(GAP103/10/0886), P(GPP202/10/P220), P(VF20102014004), S
n4:dodaniDat
n8:2015
n4:domaciTvurceVysledku
n11:3165647 n11:3445771 n11:2448815
n4:druhVysledku
n12:D
n4:duvernostUdaju
n21:S
n4:entitaPredkladatele
n14:predkladatel
n4:idSjednocenehoVysledku
231881
n4:idVysledku
RIV/00216224:14330/11:00073202
n4:jazykVysledku
n15:eng
n4:klicovaSlova
locality-sensitive hashing; metric space; similarity search; recall; stability
n4:klicoveSlovo
n5:metric%20space n5:stability n5:similarity%20search n5:locality-sensitive%20hashing n5:recall
n4:kontrolniKodProRIV
[C5AE13BE0C14]
n4:mistoKonaniAkce
Lipary, Italy
n4:mistoVydani
New York
n4:nazevZdroje
Fourth International Conference on Similarity Search and Applications, SISAP 2011
n4:obor
n22:IN
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n13:GA201%2F09%2F0683 n13:VF20102014004 n13:GAP103%2F10%2F0886 n13:GPP202%2F10%2FP220
n4:rokUplatneniVysledku
n8:2011
n4:tvurceVysledku
Novák, David Zezula, Pavel Kyselák, Martin
n4:typAkce
n10:WRD
n4:zahajeniAkce
2011-06-30+02:00
s:numberOfPages
8
n16:doi
10.1145/1995412.1995422
n19:hasPublisher
ACM Press
n20:isbn
9781450307956
n3:organizacniJednotka
14330